**Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach**

### **Junwei Cao, Wanlu Zhang, Zeqing Xiao and Haochen Hua \***

Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China; jcao@tsinghua.edu.cn (J.C.); zhangwl15@tsinghua.org.cn(W.Z.);xiaozeqing@mail.tsinghua.edu.cn(Z.X.)

**\*** Correspondence: hhua@tsinghua.edu.cn

Received: 12 March 2019; Accepted: 22 April 2019; Published: 24 April 2019

**Abstract:** The existence of high proportional distributed energy resources in energy Internet (EI) scenarios has a strong impact on the power supply-demand balance of the EI system. Decision-making optimization research that focuses on the transient voltage stability is of grea<sup>t</sup> significance for maintaining effective and safe operation of the EI. Within a typical EI scenario, this paper conducts a study of transient voltage stability analysis based on convolutional neural networks. Based on the judgment of transient voltage stability, a reactive power compensation decision optimization algorithm via deep reinforcement learning approach is proposed. In this sense, the following targets are achieved: the efficiency of decision-making is greatly improved, risks are identified in advance, and decisions are made in time. Simulations show the effectiveness of our proposed method.

**Keywords:** energy Internet; convolutional neural network; decision optimization; deep reinforcement learning
